Rao-Blackwellised particle filtering for low-cost encoder/INS/GNSS integrated vehicle navigation with wheel slipping

This study presents a Rao-Blackwellised particle filter (RBPF)-based encoder/inertial navigation system (INS)/global navigation satellite system (GNSS) integration method for improving the navigation performance of an autonomous land vehicle (ALV) with wheel slipping. In contrast to traditional integration methods, the proposed integration method introduces an overall wheel slip consideration for the ALV, which greatly improves the accuracy of the velocity estimation, especially when the inertial sensor is low cost. Additionally, the proposed integrated system uses double-difference pseudorange measurements instead of single point positioning results provided by low-cost GNSS receivers, which greatly improves the accuracy of the position estimation. To verify the navigation performance of the proposed integrated system, comparisons between the states estimated by the proposed system, the EKF-based integrated system and the joint wheel-slip and motion-estimation system are provided. The results of the experiment show that the proposed integrated system has the highest accuracy in both the position estimation and the velocity estimation among the three compared systems, and can improve the navigation performance during GNSS signals outages.